With a COVID-19 magnified mental health crisis and growing old population (10.7% of population aged over 65 is diagnosed with Alzheimer's disease and 18% is diagnosed with mild cognitive impairment (MCI)) there is an immediate need for developing systems that can better understand and characterize cognitive and mental health (CMH) by tracking various biomarkers from functional magnetic resonance imaging (fMRI), electroencephalogram (EEG), speech, electronic health record (EHR), movement, cognitive surveys, wearable devices, structured, genomic, and epigenomic data. One of the core technical opportunities for accelerating the computational analysis of CMH lies in multimodal (MM) ML: learning representations that model the heterogeneity and interconnections between diverse input signals. MM is particularly important in CMH primarily due to the presence of noisy labels and subjectivity inherent in surveys. The utilization of multiple signals and modalities offers a potential solution to overcome these challenges. In addition, it is imperative to emphasize the necessity for increased data sharing and enhanced collaboration within the CMH research community. As we endeavor to tackle the multifaceted challenges posed by cognitive and mental health disorders, a collective effort is essential to facilitate access to high-quality datasets and promote collaborative initiatives. By promoting transparency and facilitating the exchange of insights and methodologies, we can accelerate progress and drive innovation in CMH research. This workshop serves as a platform for fostering such collaboration, inviting participants to contribute their expertise and insights towards the shared goal of advancing our understanding and treatment of cognitive and mental health disorders. Together, through open dialogue and shared resources, we can chart a path towards a brighter future for individuals affected by CMH conditions.
Recently, major progress has been made in pre-trained deep and MM learning from text, speech, images, video, signals, and structured data [1, 2, 3, 4], and there has also been initial success towards using deep learning and MM streams to improve prediction of patient status or response to treatment in CMH applications [5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]. However, there remains computational and theoretical challenges that need to be solved in machine learning for CMH, spanning 1. collecting and sharing quality data for moderate and severe patients, 2. learning from many diverse and understudied signals, 3. theoretically understanding the natural way of modality connections and interactions in MM learning, 4. real-world deployment concerns such as safety, robustness, interpretability, and collaboration with various stakeholders, and 5. extending models to low resource and multilingual environments.
This workshop has three primary goals:
1. bring together experts from multiple disciplines working on ML and CMH to learn from each other, 2. encourage the development of shared goals and approaches across these communities, and 3. stimulate creation of better MM technologies for real-world CMH impact.
To achieve these goals, this workshop includes a diverse lineup of invited speakers across fields associated with ML and CMH, hosting experts from computer vision (CV), natural language processing (NLP), MM learning, signal processing, human-computer interaction, neuroscience, psychiatry, and psychology. To encourage discussion and further collaboration toward the advancement of ML for CMH, the workshop combines invited talks, contributed papers and posters, and panel discussion. In addition, organizers hosted a mentorship program with help of mentors from the program committee, similar to mentorship program of ACL-SRW 1 , in order to increase reach and to help researchers from across the world who are new to this field to improve the quality of their papers before the submission time.
This workshop contributes to the diversity of the field and increases collaboration between machine learning, psychiatry, psychology, and neuroscience researchers. It
The workshop will take place at Vancouver Convention Centre -West Building, Room 205, on February 26th, 2023. It features six keynote speakers, oral sessions, poster sessions, and panel discussion, and networking lunch. From 20 submitted papers, six were selected for oral and poster presentation and additional nine papers were selected for poster presentation only. Acceptance rate was therefore 75%. See detailed schedule in Table 2.
Further details about the workshop can be accessed at https://winterlightlabs.github.io/ml4cmh2024/.
We would like to thank you to the following people for their help and support during workshop preparation: 1) Aparna Balagopalan PhD Student at MIT; 2) Thomas Hartvigsen, PhD, Assistant Professor at University of Virginia; and 3) William Jarrold, Trade Desk.
We would like to express our sincere gratitude to Winterlight Labs 24 , Canada and Cambridge Cognition 25 , UK companies for their generous support and contribution to the success of this event. We are deeply appreciative of their support and partnership, which has been instrumental in making this event possible.